Powered leg prosthesis and control methodologies for obtaining near normal gait

ABSTRACT

A powered leg prosthesis includes powered knee joint comprising a knee joint and a knee motor unit for delivering power to the knee joint. The prosthesis also includes a prosthetic lower leg having a socket interface coupled to the knee joint and a powered ankle joint coupled to the lower leg opposite the knee joint comprising an ankle joint and an ankle motor unit to deliver power to the ankle joint. The prosthesis further includes a prosthetic foot coupled to the ankle joint, at least one sensor for measuring a real-time input, and at least one controller for controlling movement of the prosthesis based on the real-time input.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. Non-Provisional applicationSer. No. 12/427,384 entitled “POWERED LEG PROSTHESIS AND CONTROLMETHODOLOGIES FOR OBTAINING NEAR NORMAL GAIT”, filed Apr. 21, 2009,which claims the benefit of Provisional Application Ser. No. 61/046,684entitled “POWERED LEG PROSTHESIS AND CONTROL METHODOLOGIES FOR OBTAININGNEAR NORMAL GAIT”, filed Apr. 21, 2008, both of which are hereinincorporated by reference in their entirety.

FEDERAL RIGHTS STATEMENT

This invention was made with government support under grant No.R01EB005684-01 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

FIELD OF THE INVENTION

The invention relates to a powered leg prosthesis and controlmethodologies for controlling the prosthesis.

BACKGROUND

Leg prostheses can provide an artificial ankle, and artificial knee, orboth an artificial ankle and an artificial knee. A transfemoralprosthesis is a prosthesis designed for above the knee amputees.Transfemoral prostheses are generally more complicated than transtibialprostheses, as they must include a knee joint.

Nearly all current commercial transfemoral comprising prostheses areenergetically passive devices. That is, the joints of the prostheseseither store or dissipate energy, but do not provide net power over agait cycle. The inability to deliver joint power impairs the ability ofthese prostheses to restore many locomotive functions, including walkingup stairs and up slopes. Moreover, there is a need for a leg prosthesisthat provides a more natural gait behavior.

SUMMARY

This Summary is provided to comply with 37 C.F.R. §1.73, presenting asummary of the invention to briefly indicate the nature and substance ofthe invention. It is submitted with the understanding that it will notbe used to interpret or limit the scope or meaning of the claims.Embodiments of the invention provide power leg prostheses and associatedmethods for control.

In a first embodiment of the invention, a powered leg prosthesis isprovided. The prosthesis includes a powered knee joint comprising a kneejoint and a knee motor unit for delivering power to the knee joint, aprosthetic lower leg having a socket interface above the knee joint, apowered ankle joint coupled to the lower leg opposite the knee jointcomprising an ankle joint and an ankle motor unit to deliver power tothe ankle joint, and a prosthetic foot coupled to the ankle joint. Theprosthesis also includes at least one sensor for measuring a real-timeinput, and at least one controller for controlling movement of theprosthesis based on the real-time input.

In a second embodiment of the invention, a method is provided forcontrolling a powered leg prosthesis comprising at least one of an ankleand a knee joint, the leg prosthesis coupled to a user of theprosthesis. The method includes representing behavior of the at leastone joint as a plurality of different activity modes, and within eachactivity mode a plurality of different internal phases, and responsiveto sensing of at least one input initiated by the user, switchingbetween the internal phases and activity modes. In the method, netenergy can be delivered to the at least one joint upon the switchingbetween the internal phases, and no net energy is delivered to the atleast one joint if the internal phases remain unchanged.

In a third embodiment of the invention, a powered leg prosthesis isprovided. The prosthesis includes a powered knee joint comprising a kneejoint and a knee motor unit for delivering power to the knee joint, aprosthetic lower leg having a socket interface above the knee joint, apowered ankle joint coupled to the lower leg opposite the knee jointcomprising an ankle joint and an ankle motor unit to deliver power tothe ankle joint, and a prosthetic foot including a ball and a heel. Theprosthesis also includes at least one sensor for providing a sagittalplane moment, and ground interaction forces at the ball and at the heel,and at least one controller coupled to the sensor for extractingreal-time input from the user based on data from the sensor forcontrolling movement of the prosthesis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a view of a powered knee and ankle prosthesis, according toan embodiment of the invention.

FIG. 1B is an exploded view of the powered knee and ankle prosthesisshown in FIG. 1A, according to an embodiment of the invention.

FIG. 2 is an exploded view of knee motor unit, according to anembodiment of the invention.

FIG. 3 is an exploded view of ankle motor unit, according to anembodiment of the invention

FIG. 4 is an exploded view of knee joint, according to an embodiment ofthe invention.

FIG. 5 is an exploded view of ankle joint, according to an embodiment ofthe invention.

FIGS. 6A and B are views of a foot having toe and heel force sensingelements, according to an embodiment of the invention.

FIG. 7 shows the joint angle and torque convention used herein. Positivetorque is defined in the direction of increasing angle.

FIG. 8 shows the subdivision of normal walking into four internal phasesshowing the knee and ankle angles during the phases, according to anembodiment of the invention.

FIG. 9 shows a finite-state model of normal walking, according to anembodiment of the invention. Each box represents a different internalphase and the transition conditions between the internal phases arespecified.

FIG. 10 shows piecewise fitting of knee and ankle torques during normalspeed level walk scaled for a 75 kg adult to a non-linear spring-damperimpedance model.

FIG. 11 is a diagram for an active/passive decomposition based controlof the powered knee and ankle prosthesis, according to an embodiment ofthe invention.

FIG. 12 is a diagram for a general form of active-passive decompositioncontrol including intent recognition that provides supervisorymodulation, according to an embodiment of the invention.

FIG. 13A is a side view of powered knee and ankle prosthesis, accordingto another embodiment of the invention.

FIG. 13B is a front view of powered knee and ankle prosthesis of FIG.13A.

FIGS. 14A and 14B show perspective and bottom views of an exemplarysagittal moment load cell suitable for use in the various embodiments ofthe invention.

FIG. 15 is a block diagram of an exemplary embedded microcontroller inaccordance with an embodiment of the invention.

FIG. 16 is a control state chart for the three activity modescorresponding to walking, standing, and sitting, and for the internalphases and their corresponding transitions within each activity mode.

FIG. 17 shows knee angle modulated knee stiffness during pre-stand(solid line) and pre-sit (dashed line) phases.

FIG. 18 is a plot of axial actuation unit force versus ankle angle.

FIG. 19 shows a normal speed walking phase portrait of the knee jointand four stride segments.

FIG. 20 shows the selection of indexing data samples during a firstsegment of a walking stride.

FIGS. 21A, 21B, and 21C are the output of the decomposition for Segment1 showing the spring and dashpot constants and the active and passiveknee torques.

FIG. 22 is a state chart for governing the discrete dynamics of anactive-passive decomposition controller in accordance with an embodimentof the invention.

FIG. 23 is a state chart for governing the discrete dynamics of thecadence estimator in accordance with an embodiment of the invention.

FIG. 24 is a schematic diagram of accelerometer measurements for slopeestimation in accordance with an embodiment of the invention.

FIG. 25 is a state chart for slope estimation in a controller inaccordance with an embodiment of the invention.

FIGS. 26A and 26B show front and back views of a friction/cable drivemotor in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

The invention is described with reference to the attached figures,wherein like reference numerals are used throughout the figures todesignate similar or equivalent elements. The figures are not drawn toscale and they are provided merely to illustrate the instant invention.Several aspects of the invention are described below with reference toexample applications for illustration. It should be understood thatnumerous specific details, relationships, and methods are set forth toprovide a full understanding of the invention. One having ordinary skillin the relevant art, however, will readily recognize that the inventioncan be practiced without one or more of the specific details or withother methods. In other instances, well-known structures or operationsare not shown in detail to avoid obscuring the invention. The inventionis not limited by the illustrated ordering of acts or events, as someacts may occur in different orders and/or concurrently with other actsor events. Furthermore, not all illustrated acts or events are requiredto implement a methodology in accordance with the invention.

The present inventors have observed that biomechanically normal walkingrequires positive power output at the knee joint and significant netpositive power output at the ankle joint. Embodiments of the inventionprovide a prosthesis that delivers power at both the knee and anklejoints. Unlike prior disclosed leg prosthetics that generate a desiredjoint trajectory for the prosthetic leg based on measurement of thesound leg trajectory and thus requires instrumentation of the sound leg,embodiments of the invention do not generally require instrumentation ofthe sound leg. Prostheses including transfemoral prostheses according toembodiments of the invention generally provide power generationcapabilities comparable to an actual limb and a gait-based controlframework for generating the required joint torques for locomotion whileensuring stable and coordinated interaction with the user and theenvironment. Embodiments of the invention thus enable the restoration ofsubstantially biomechanically normal locomotion.

One design for a prosthesis according to an embodiment of the inventionis shown in FIG. 1A through FIG. 6B. The prosthesis 100 comprises aprosthetic lower leg 101. Lower leg 101 can be coupled to a powered kneejoint comprising a knee motor unit 105 coupled to a knee joint 110, anda powered ankle joint comprising an ankle motor 115 coupled to an anklejoint 120. A sagittal plane moment sensor 125 can be located between theprosthesis and the user to measure the moment, and in one embodiment islocated immediately below the socket interface. In the embodiment shown,sensor 125 measures the sagittal plane moment, while separate sensorsdescribed below measure the ball of foot force and heel force withrespect to the ground or other object the foot is pressed against. Aload sensor 135 can be positioned at the ball of the foot, and a loadsensor 140 can be positioned at the heel of the foot. However, inanother embodiment (not shown) sensor 125 can measure the sagittal planemoment, the frontal plane moment and the axial force, such as providedby the three-axis socket load cell. This alternate embodiment caneliminate the need for sensor 135 and sensor 140.

Load sensors 141 and 142 are in series with each motor unit 105 and 115,respectively for motor unit force control. Position sensors 151 and 152are provided at each joint 110 and 120 as shown in FIGS. 4 and 5respectively. Position sensors 151 measure joint angle (θ as used below)and can be embodied as potentiometers. The computer/process controller,and power source (e.g. a battery such as a Li ion battery, andelectrical connections in the case of an electrical power source are notshown to avoid obscuring aspects of the invention. Non-electrical powersources may also be used, such as pneumatic power, or non-batteryelectrical sources, such as hydrogen-based fuel cells.

Prosthesis 100 is shown in an exploded view in FIG. 1B. Joints 110 and120 are more clearly shown as compared to FIG. 1A.

FIG. 2 is an exploded view of knee motor unit 105, according to anembodiment of the invention. Load sensor 141 is shown as a load cell(e.g. strain gauge). Load sensor 141 measures force and moments. Themotor unit 105 comprises a motor-driven ball screw assembly which drivesthe knee joint through a slider-crank linkage comprising screw 145.Other motor drive assemblies may also generally be used.

FIG. 3 is an exploded view of ankle motor unit 115, according to anembodiment of the invention. Load sensor 142 is generally analogous toload sensor 141. The motor unit 115 comprises a motor-driven ball screwassembly which drives the ankle joint through a slider-crank linkagecomprising screw 145. The ankle motor 115 includes a spring 147positioned to provide power in parallel (thus being additive) with powerprovided by the motor unit 115. Spring 147 biases the motor unit's forceoutput toward ankle plantarflexion, and supplements the power outputprovided by motor unit 115 during ankle push off.

FIG. 4 is an exploded view of knee joint 110, according to an embodimentof the invention. As described above, knee joint 110 includes positionsensor 151 that can be embodied as a potentiometer for anglemeasurements of the knee joint 110.

FIG. 5 is an exploded view of ankle joint 120, according to anembodiment of the invention. As described above, ankle joint 120includes position sensor 152 that can be embodied as a potentiometer forangle measurements of the ankle joint 120.

FIG. 6A is a view of a foot 170 having ball of foot sensors 135,according to an embodiment of the invention. Sensors 135 are provided tomeasure the ground reaction forces near the ball of the foot, such aswhen the foot strikes the ground. FIG. 6B is a view of a foot 170 havingball of foot sensors 135 and heel sensors 140, according to anembodiment of the invention. Sensors 140 are provided to measure theground reaction forces on the heel of the foot when the foot 170 strikesthe ground. Sensors 135 and 140 can be embodied as strain based sensors.

As described above, prostheses according to embodiments of the inventiongenerally provide a gait-based control framework for generating therequired joint torques for locomotion while ensuring stable andcoordinated interaction with the user and the environment. This enablesembodiments of the invention to restore substantially biomechanicallynormal locomotion.

Regarding control of the prosthesis, conventional prosthetic controlschemes utilize position-based control which comprises generation of adesired joint angle/position trajectories, which by its nature, mustutilize the prosthesis itself as a position source (e.g. “echo-control”based approaches). Such an approach poses several problems for thecontrol of a powered prosthesis, such as prostheses according toembodiments of the invention. First, the desired position trajectoriesare typically computed based on measurement of the sound side (normal)leg trajectory, which 1) is not well suited to bilateral amputees, 2)requires instrumentation of the sound side leg, and 3) generallyproduces an even number of steps, which can present a problem when theuser desires an odd number of steps. A subtle yet significant issue withconventional position-based control is that suitable motion trackingrequires a high output impedance (i.e., joints must be stiff), whichforces the amputee to react to the limb rather than interact with ormore generally control the prosthetic limb. Specifically, in order forthe known prosthesis to dictate the joint trajectory, it must generallyassume a high output impedance, thus substantially precluding dynamicinteraction with the user and the environment.

Unlike existing passive prostheses, the introduction of power into aprosthesis according to embodiments of the invention provides theability for the device to also act, rather than simply react. As such,the development of a suitable controller and control methodology thatprovides for stable and reliable interaction between the user andprosthesis is provided herein. Control according to embodiments of theinvention has been found to enable the user to interact with theprosthesis by leveraging its dynamics in a manner similar to normalgait, and also generates more stable and more predictable behavior.

Thus, rather than gather user intent from the joint angle measurementsfrom the contralateral unaffected leg, embodiments of the inventioninfer commands from the user via the (ipsilateral) forces and moments ofinteraction between the user and prosthesis. Specifically, the userinteracts with the prosthesis by imparting forces and moments from theresidual limb to the prosthesis, all of which can be measured viasuitable sensor(s), such as sensors 125, 140 and 141 described abovewhich measures moments/forces. These forces and moments serve not onlyas a means of physical interaction, but also serve as an implicitcommunication channel between the user and device, with the user'sintent encoded in the measurements. Inferring the user's intent from themeasured forces and moments of interaction according to embodiments ofthe invention provides several advantages relative to the known echoapproach.

In one embodiment of the invention the torque required at each jointduring a single stride (i.e. a single period of gait) can be piecewiserepresented by a series of passive impedance functions. A regressionanalysis of gait data indicates that joint torques can be characterizedby functions of joint angle (θ) and angular velocity by an impedancemodel, such as the following exemplary passive impedance function shownin equation 1 below:τ=k ₁(θ−θ_(e))+b*{dot over (θ)}  (1)where k₁, b, and the equilibrium joint angle θ_(e) are all constantsthat are generally generated empirically, and are constants for a givenjoint during a given internal phase (e.g. knee, internal phase 3). k₁characterizes the linear stiffness. b is the linear damping coefficient,θ is the measured joint angle which can characterize the state of theprosthesis, θ_(e) is the equilibrium angle, {dot over (θ)} is theangular velocity of the joint, and τ is the joint torque. Given theseconstants, together with instantaneous sensor measurements for θ and{dot over (θ)}, the torque (τ) at the joints (knee and ankle) can bedetermined.

Positive directions of the angle (θ) and torque (τ) as used herein aredefined as shown in FIG. 7. If the coefficients b and k₁ are constrainedto be positive, then the joints will each exponentially converge to astable equilibrium at θ=θ_(e) and {dot over (θ)}=θ within each internalphase. That is, within any given internal phase, the actuators areenergetically passive (i.e. the joint will come to rest at a localequilibrium). While the unactuated prosthesis can be energeticallypassive, the behavior of one joint (knee or ankle) or the combinedbehavior of the knee and ankle joints, can be likewise passive, and thuswill generally respond in a predictable manner.

Responsive to direct input from the user (e.g. a heel strike) to triggera change in internal phase, power (torque) can be delivered from thepower source (e.g. battery) to the prosthesis in the proper magnitude toprovide the desired movement. Since the switching can be triggered bydirect input from the user related to the current internal phase, theuser maintains direct influence over the power applied to theprosthesis. If the user does not trigger the next internal phase (i.e.remains stationary) no net energy is delivered. That is, the prosthesiswill generally cease to receive power from the power source for movingthe joint, and will instead, due to the damped response, soon come torest at the local equilibrium identified with the present internalphase.

As described above, the decomposition of joint behavior into passivesegments requires the division of the gait cycle into a plurality ofinternal phases or “finite states” characterized by an impedancefunction and a set of constants for the impedance function, as dictatedby their functions and the character of the piecewise segments of theimpedance functions described above. The switching rules betweeninternal phases should generally be well defined and measurable, and thenumber of phases should be sufficient to provide a substantiallyaccurate representation of normal joint function. In one embodiment ofthe invention, the swing and stance phase of gait can constitute aminimal set of internal phases.

Based on least-squares regression fitting of Equation 1 to empiricalgait data, the present Inventors determined that such fits were improvedsignificantly by further dividing the two modes of swing and stance eachinto two additional internal phases to realize four phases, as shown inFIG. 8. A fifth internal phase can also be added, as illustrated in FIG.16. The angle (θ) of the prosthetic knee (above) and ankle joint (below)can be provided during each internal phase as a function of the % of thestride. Angle values shown can be used as threshold values to triggerphase changes as described below relative to FIG. 9. As clear to onehaving ordinary skill in the art, the number of phases can be other thantwo or four.

FIG. 9 shows exemplary switching rules between internal phases forwalking FIG. 16 shows another set exemplary switching rules, forwalking, standing, and sitting activity modes. As described above, ifthe user does not initiate actions that trigger the next phase (e.g.based on the switching rules), the prosthesis will cease to receivepower and will come to rest at the local equilibrium identified with thepresent phase. For example, switching can be based on the ankle angle>athreshold value (mode 1 to mode 2), or ankle torque<threshold) (mode 2to mode 3), the angle or torque measurements provided by on boardsensors as described above.

Phase 1 shown in FIG. 8 begins with a heel strike by the user (which canbe sensed by the heel force sensor), upon which the knee immediatelybegins to flex so as to provide impact absorption and begin loading,while the ankle simultaneously plantarflexes to reach a flat foot state.Both knee and ankle joints have relatively high stiffness (and can beaccounted for by k1 in equation 1) during this phase to prevent bucklingand allow for appropriate stance knee flexion, because phase 1 comprisesmost of the weight bearing functionality. Phase 2 is the push-off phaseand begins as the ankle dorsiflexes beyond a given angle (i.e. user'scenter of mass lies forward of stance foot). The knee stiffnessdecreases in this mode to allow knee flexion while the ankle provides aplantarflexive torque for push-off. Phase 3 begins as the foot leavesthe ground as detected by the ankle torque load cell and lasts until theknee reaches maximum flexion. Mode 4 is active during the extension ofthe knee joint (i.e. as the lower leg swings forward), which begins asthe knee velocity becomes negative and ends at heel strike (e.g. asdetermined by the heel force sensor).

In both of the swing phases (Phases 3 and 4), the ankle torque can besmall and can be represented in the controller as a (relatively) weakspring regulated to a neutral position. The knee can be primarilytreated as a damper in both swing phases.

Impedance modeling of joint torques was preliminarily validated byutilizing the gait data from a healthy 75 kg subject, as derived frombody-mass normalized data. Incorporating the four internal phasesdescribed above, along with the motion and torque data for each joint, aconstrained least-squares optimization was conducted to generate a setof parameters k₁, b and θ_(e) for each phase for each joint for use inEquation 1. The resulting parameter set can be fit to joint torques andis shown graphically in FIG. 10. FIG. 10 shows piecewise fitting of kneeand ankle torques during normal speed level walk scaled for a 75 kgadult to a non-linear spring-damper impedance model. The numbers shownin each phase represent the mean ratio of the stiffness forces todamping forces predicted by the fit. The vertical lines represent thesegmentation of a gait stride into four distinct phases. The fit shownin FIG. 10 clearly indicates that normal joint function can berepresented by the use of piecewise passive functions.

Controllers according to embodiments of the invention generally comprisean underlying gait controller (intra-modal controller). An optionalsupervisory gait controller (also called intent recognizer) can also beprovided. Both controllers generally utilize measured information. Thisinformation generally comprises user and ground interaction forces (F)and moments/torques (τ), joint angles and angular velocities fromon-board sensors, and can be used to extract real-time input from theuser. The gait control component utilizes the sensed instantaneousnature of the user input (i.e., moments and forces) to control thebehavior of the leg within a given activity mode, such as standing,walking, or stair climbing.

Two exemplary approaches to intra-modal impedance generation aredescribed below. The first approach is shown in FIG. 11 and represents ageneral form of active-passive decomposition-based intra-mode control.The second embodiment shown in FIG. 12 includes the control structureshown in FIG. 11 but adds a supervisory intent recognizing controller tomodulate the intra-modal control based on inputs from an intentrecognition module. As shown in FIGS. 11 and 12, F_(s) is the force theuser of the prosthesis is applying, such as a heel force in the case ofa heel strike, τ represents joint torque, and θ represent joint angles.τ_(a) represents the active component of joint torque which is roughlyproportional to the input force, and τ_(p) represents the passivecomponent of torque. The active joint torque τ_(a) is thus the totaljoint torque τ minus the passive joint torque, τp. Derivatives are shownusing the dot convention, with one dot being the first derivative (e.g.,{dot over (θ)} being angular velocity) and two dots representing thesecond derivative.

Intra-Modal Active-Passive Decomposition Control

In this embodiment of the intra-modal controller, shown in FIG. 11, thebehavior of the prosthesis can be decomposed into a passive componentand an active control component. The active control component is analgebraic function of the user's real-time input F_(s) (i.e., sensedsocket-prosthesis interface forces and moments and sensed groundreaction forces). The controller output is shown as the active torque(τ_(a)) minus the passive torque τ_(p). The controller outputτ_(a)−τ_(p) applied to the prosthetic leg based on dynamics of the legresponds via θ and {dot over (θ)}. The system response, θ and {dot over(θ)}, is fed back to the controller.

Power applied to the prosthesis can be thus commanded directly by theuser through measured interface forces and moments initiated by usermovements. In the absence of these commands from the user, F_(s)=0,τ_(a)=0 and the prosthesis fundamentally (by virtue of the controlstructure) cannot generate power, and thus only exhibits controlledpassive behavior. Due to the decomposition of energetic behaviorsinherent in this control structure, the prosthesis under it's owncontrol can be generally stable and passive. Unlike known echo controlapproaches, the input can be real-time, based only on the affected leg,and thus the approach can be equally applicable to bilateral andunilateral amputees and can reflect the instantaneous intent of theuser. Additionally, unlike echo control that is based on servocontrol,the prosthesis will exhibit a natural impedance to the user that shouldfeel more like a natural limb. These combined features should result inan active prosthesis that will feel inasmuch as possible like a naturalextension of the user. The structure and properties of both the gaitcontroller and intent recognizer are described below.

As described above, since gait is largely a periodic activity, jointbehavior can be functionally decomposed over a period by decomposing thejoint torque into a passive component and an active component. Thepassive component can comprise a function of angle (i.e., single-valuedand odd), and a function of angular velocity passive (i.e.,single-valued and odd), such as equation 1 described above. The activecomponent can be a function of the user input (i.e., socket interfaceforces). Given a set of data that characterizes a nominal period ofjoint behavior, the passive component can be first extracted from thewhole, since the passive behavior is a subset of the whole (i.e., thepassive component consists of single-valued and odd functions, while theactive has no restrictions in form). The passive component can beextracted by utilizing a least squares minimization to fit a generalizedsingled-valued odd function of angle and angular velocity to the torque.Once the passive component is extracted, the residual torque (i.e., theportion that is not extracted as a passive component), can beconstructed as an algebraic function of the sensed socket interface andground reaction forces (i.e., the direct-acting user input) byincorporating a similar candidate function, but not restricted to be ofpassive form. Finally, superimposing the passive and active componentsprovides a decomposed functional approximation of the original periodjoint torque.

Intra-Modal Locally Passive Event-Triggered Control

In this embodiment of the intra-modal controller shown in FIG. 12, asupervisory intent recognizer can be added that utilizes the same senseduser inputs (i.e., moments and forces) as the intra-modal/gaitcontroller, but extracts the user's intent based on the characteristicshape of the user input(s) and system response (e.g. F, θ, θ-dot). Basedon the extracted intent, the supervisory intent recognizer modulates thebehavior of the underlying gait controller to smoothly transitionbehavior within a gait (e.g., speed and slope accommodation) and betweengaits (e.g., level walk to stair ascent), thus offering a unifiedcontrol structure within and across all gaits.

Gait intent recognition can be a real time pattern recognition or signalclassification problem. The signal in this case is generally thecombination of socket interface forces Fs and the dynamic state of theprosthesis, which in one embodiment can be a vector of the knee andankle angles θ for a powered leg prosthesis according to an embodimentof the invention. A variety of methods exist for pattern recognition andsignal classification including nearest neighbor algorithms, neuralnetworks, fuzzy classifiers, linear discriminant analysis, and geneticalgorithms.

Sensors

As described above embodiments of the invention include a number ofsensors for providing signals for adjusting operation of a leg and ankleprosthesis. A description of one exemplary arrangement of sensors can bedescribed below with respect to FIGS. 13A, 13B, 14A, and 14B. FIG. 13Ais a side view of powered knee and ankle prosthesis 1300, according toanother embodiment of the invention. FIG. 13B is a front view of poweredknee and ankle prosthesis of FIG. 13A. FIGS. 14A and 14B showperspective and bottom views of an exemplary sagittal moment load cellsuitable for use in the various embodiments of the invention.

Each joint actuation unit, such as knee actuation unit 1302 and ankleactuation unit 1304 in FIG. 13A, can include a uniaxial load cellpositioned in series with the actuation unit for closed loop forcecontrol. Both the knee and ankle joints can incorporate integratedpotentiometers for joint angle position. The ankle actuation unit caninclude a spring 1305, as described above with respect to FIGS. 1A-4.One 3-axis accelerometer can be located on the embedded system 1306 anda second one can located below the ankle joint 1308 on the ankle pivotmember 1310. A strain based sagittal plane moment sensor 1312, such assensor 1400 shown in FIGS. 14A and 14B, can located between the kneejoint 1314 and the socket connector 1316, which measures the momentbetween a socket and the prosthesis. In the various embodiments of theinvention, a sagittal plane moment sensor can be designed to have a lowprofile in order to accommodate longer residual limbs. The sensor canincorporate a full bridge of semiconductor strain gages which measurethe strains generated by the sagittal plane moment. In one embodiment ofthe invention, the sagittal plane moment sensor was calibrated for ameasurement range of 100 Nm. A custom foot 1318 can designed to measurethe ground reaction force components at the ball 1320 of the foot andheel 1322. The foot can include of heel and ball of foot beams, rigidlyattached to a central fixture and arranged as cantilever beams with anarch that allows for the load to be localized at the heel and ball ofthe foot, respectively. Each heel and ball of foot beam can alsoincorporates a full bridge of semiconductor strain gages that measurethe strains resulting from the respective ground contact forces. In oneembodiment of the invention, the heel and ball of foot load sensors werecalibrated for a measurement range of 1000 N. In addition, incorporatingthe ground reaction load cell into the structure of a custom foot caneliminate the added weight of a separate load cell, and also enablesseparate measurement of the heel and ball of foot load. The prostheticfoot can be designed to be housed in a soft prosthetic foot shell (notshown).

Microcontroller System

The powered prosthesis contains an embedded microcontroller that allowsfor either tethered or untethered operation. An exemplary embeddedmicrocontroller system 1500 is shown in the block diagram in FIG. 15.The embedded system 1500 consists of signal processing, power supply,power electronics, communications and computation modules. The systemcan be powered by a lithium polymer battery with 29.6 V. The signalelectronics require +/−12 V and +3.3 V, which are provided via linearregulators to maintain low noise levels. For efficiency, the batteryvoltage can be reduced by PWM switching amplifiers to +/−15 V and +5 Vprior to using the linear regulators. The power can be disconnected viaa microcontroller that controls a solid state relay. The power statuscan be indicated by LED status indicators controlled also by themicrocontroller.

The analog sensor signals acquired by the embedded system include theprosthesis sensors signals (five strain gage signals and twopotentiometer signals), analog reference signals from the laptopcomputer used for tethered operation, and signals measured on the boardincluding battery current and voltage, knee and ankle servo amplifiercurrents and two 3-axis accelerometers. The prosthesis sensor signalsare conditioned using input instrumentation amplifiers. The battery,knee motor and ankle motor currents are measured by current senseresistors and current sensing amplifiers. The signals are filtered witha first-order RC filter and buffered with high slew rate operationalamplifiers before the analog to digital conversion stage. Analog todigital conversion can be accomplished by two 8-channel analog todigital convertors. The analog to digital conversion data can betransferred to the microcontroller via serial peripheral interface (SPI)bus.

The main computational element of the embedded system can be a 32-bitmicrocontroller. In the untethered operation state, the microcontrollerperforms the servo and activity controllers of the prosthesis and datalogging at each sample time. In addition to untethered operation, theprosthesis can also be controlled via a tether by a laptop computerrunning MATLAB Simulink RealTime Workshop. In the tethered operationstate, the microcontroller drives the servo amplifiers based on analogreference signals from the laptop computer. A memory card can be usedfor logging time-stamped data acquired from the sensors and recordinginternal controller information. The memory chip can be interfaced tothe computer via wireless USB protocol. The microcontroller sends PWMreference signals to two four quadrant brushless DC motor drivers withregenerative capabilities in the second and forth quadrants of thevelocity/torque curve.

Control of Sitting and Standing

In some embodiments of the invention, additional controls can beprovided for operating the prosthesis when going from a sitting to astanding position or vice versa. This can be implemented via the use ofa sitting mode controller implemented in the microcontroller. Operationof the sitting mode controller consists of four phases that are outlinedin the general control state chart shown in FIG. 16. As shown in FIG.16, two phases are primary sitting phases, weight bearing and non-weightbearing. The other two phases encompass the transition phases, pre-standand pre-sit, for standing up and sitting down, respectively. Weightbearing and non-weight bearing are the primary sitting phases thatswitch the knee and ankle joints between high and low impedances,respectively. The transition phases, pre-stand and pre-sit, modulate thestiffness of the knee as a function of knee angle, as shown in FIG. 17,to assist the user in standing up and sitting down. FIG. 17 shows kneeangle modulated knee stiffness during pre-stand (solid line) and pre-sit(dashed line) phases.

The modulation allows for smoother transitions near the seated position.The ankle joint can be slightly dorsiflexed with moderate stiffnessduring the standing up and sitting down phases. Switching between thefour sitting phases occurs when sensor thresholds are exceeded, asdepicted FIG. 16. The parameters of the impedance based controllers aretuned using a combination of feedback from the user and joint angle,torque and power data from the prosthesis.

Mechanical Design

In the various embodiments of the invention, actuation for theprosthesis can be provided by two motor-driven ball screw assembliesthat drive the knee and ankle joints, respectively, through aslider-crank linkage. The prosthesis can be capable of 120° of flexionat the knee and 45° of planterflexion and 20° of dorsiflexion at theankle. In one embodiment, each actuation unit consists of a DC motor(such as a Maxon EC30 Powermax) connected to a 12 mm diameter ball screwwith 2 mm pitch, via helical shaft couplings. An exemplary ankleactuation unit additionally incorporates a 302 stainless steel spring(51 mm free length and 35 mm outer diameter), with 3 active coils and astiffness of 385 N/cm in parallel with the ball screw.

As described above with respect to FIGS. 1A-4, the purpose of the springcan be to bias the motor's axial force output toward ankleplantarflexion, and to supplement power output during ankle push off.The stiffness of the spring can be maximized to allow for peak forceoutput without limiting the range of motion at the ankle. The resultingaxial actuation unit's force versus ankle angle plot can be shown inFIG. 18. FIG. 18 is a plot if axial force as a function of ankle angleillustrating spring force, actuator force and total force. FIG. 18graphically demonstrates for fast walking the reduction in linear forceoutput supplied by the motor at the ankle through the addition of thespring. Note that the compression spring does not engage untilapproximately five degrees of ankle plantarflexion. Each actuation unitcan include a uniaxial load cell (such as Measurement SpecialtiesELPF-500L), positioned in series with the actuation unit for closed loopforce control of the motor/ballscrew unit. Both the knee and anklejoints can incorporate bronze bearings and, for joint angle measurement,integrated precision potentiometers (such as an ALPS RDC503013). Astrain based sagittal plane moment sensor, as previously described withrespect to FIGS. 14A and 14B can belocated between the knee joint andthe socket connector, which measures the moment between the socket andprosthesis. The ankle joint connects to a foot, which incorporatesstrain gages to measure the ground reaction forces on the ball of thefoot and on the heel. The central hollow structure houses alithium-polymer battery and provides an attachment point for theembedded system hardware. To better fit with an anthropomorphicenvelope, the ankle joint can be placed slightly anterior to thecenterline of the central structure. This gives the prosthesis theillusion of flexion when the amputee can be standing vertically with theknee fully extended.

The length of the shank segment can be varied by changing the length ofthree components; the lower shank extension, the spring pull-down, andthe coupler between the ball nut and ankle. Additional adjustability canbe provided by the pyramid connector that can be integrated into thesagittal moment load cell for coupling the prosthesis to the socket (asis standard in commercial transfemoral prostheses). In one embodiment ofthe invention, the self-contained transfemoral prosthesis was fabricatedfrom 7075 aluminum and has a total mass of 4.2 kg, which can be withinan acceptable range for transfemoral prostheses, and comparable to anormal limb segment. A weight breakdown of an exemplary device ispresented below in Table I.

TABLE II MASS BREAKDOWN OF SELF- CONTAINED POWERED PROSTHESIS. ComponentMass (kg) Battery 0.62 Electronics 0.36 Knee Motor Assembly 0.72 AnkleMotor Assembly 0.89 Sensorized Foot 0.35 Foot Shell 0.24 Sagittal MomentSensor 0.12 Remaining Structure 0.90 Total Weight 4.20 ACTIVE PASSIVETORQUE DECOMPOSITION

Passive joint torque, τ_(p), can be defined as the part of the jointtorque, τ, which can be represented using spring and dashpotconstitutional relationships (passive impedance behavior). The systemcan only store or dissipate energy due to this component. The activepart can be interpreted as the part which supplies energy to the systemand the active joint torque can be defined as τ_(a)=τ−τ_(p). This activepart can be represented as an algebraic function of the user input viathe mechanical sensory interface (i.e. socket interface forces andtorques).

Gait is considered a mainly periodic phenomena with the periodscorresponding to the strides. Hence, the decomposition of a stride willgive the required active and passive torque mappings for a specificactivity mode. In general, the joint behavior exhibits varying activeand passive behavior in each stride. Therefore, segmenting of the stridein several parts can be necessary. In this case, decomposition of thetorque over the entire stride period requires the decomposition of thedifferent segments and piecewise reconstruction of the entire segmentperiod. In order to maintain passive behavior, however, the segmentscannot be divided arbitrarily, but rather can only be segmented when thestored energy in the passive elastic element is zero. This requires thatthe phase space can only be segmented when the joint angle begins andends at the same value. FIG. 19 shows the phase portrait of normal speedwalking and the four different stride segments, S₁, S₂, S₃ and S₄. Thus,the entire decomposition process consists of first appropriatesegmentation of the joint behavior, followed by the decomposition ofeach segment into its fundamental passive and active components.

The decomposition of each segment shown in FIG. 19 can be converted toan optimization problem. In each segment of the stride, 2n data pointsare selected by sampling the angular position in equal intervals betweenits minimum and maximum and selecting the corresponding positive andnegative angular velocities. In this work, the number of angularposition samples for each segment, n can be set to be 100. Theconstrained least squares optimization problem given in Equation 2 belowcan be constructed and solved.

$\begin{matrix}{{\min\limits_{x}{\frac{1}{2}{{{Cx} - d}}_{2}^{2}\mspace{14mu}{s.t.\mspace{14mu} 0}}} \leq x} & (2)\end{matrix}$where C, x and d are defined in Equations 3, 4, and 5 below,respectively. The indexing of the joint angular position, angularvelocity and moment samples are explained via the sketch in FIG. 20.FIG. 20 shows a selection and indexing of data samples from a firstsegment.

$\begin{matrix}{{{{C_{4{nx}\; 3n} = \begin{bmatrix}C_{1} & C_{2} & C_{3}\end{bmatrix}^{T}}C_{1} = \left\lbrack {\begin{matrix}{{diag}\left( {\begin{bmatrix}\theta_{1} \\\theta_{2} \\\vdots \\\theta_{n}\end{bmatrix}_{nx1} - \alpha} \right)} \\{{diag}\left( {\begin{bmatrix}\theta_{n} \\\theta_{n - 1} \\\vdots \\\theta_{1}\end{bmatrix}_{{nx}\; 1} - \alpha} \right)}\end{matrix}{{diag}\left( \begin{bmatrix}{\overset{.}{\theta}}_{1} \\{\overset{.}{\theta}}_{2} \\\vdots \\\vdots \\\vdots \\{\overset{.}{\theta}}_{n}\end{bmatrix}_{2{nx}\; 1} \right)}} \right\rbrack_{2{nx}\; 3n}}C_{2} = \left\lbrack {\begin{matrix}C_{21} \\C_{22}\end{matrix}C_{23}} \right\rbrack_{{2n} - {1x\; 3n}}}{C_{21} = \begin{bmatrix}\theta_{1} & {- \theta_{2}} & 0 & \ldots & 0 \\0 & \ddots & \ddots & \ddots & \vdots \\\vdots & \ddots & \theta_{n - 1} & \theta_{n} & 0 \\0 & \ldots & 0 & 0 & 0\end{bmatrix}_{nxn}}{C_{22} = \begin{bmatrix}\theta_{n} & {- \theta_{n - 1}} & 0 & \ldots & 0 \\0 & \ddots & \ddots & \ddots & \vdots \\\vdots & \ddots & \theta_{3} & {- \theta_{2}} & 0 \\0 & \ldots & 0 & \theta_{2} & {- \theta_{1}}\end{bmatrix}_{n - {1\;{xn}}}}{C_{23} = \begin{bmatrix}{\overset{.}{\theta}}_{1} & {- {\overset{.}{\theta}}_{n - 1}} & 0 & \ldots & 0 \\0 & \ddots & \ddots & \ddots & \vdots \\\vdots & \ddots & {\overset{.}{\theta}}_{{2n} - 2} & {- {\overset{.}{\theta}}_{{2n} - 1}} & 0 \\0 & \ldots & 0 & {\overset{.}{\theta}}_{{2n} - 1} & {- {\overset{.}{\theta}}_{2n}}\end{bmatrix}_{{2n} - {1x\; 2n}}}{C_{3} = \begin{bmatrix}\beta & \beta & \ldots & \ldots & \ldots & \beta & \beta\end{bmatrix}_{1x\; 3\; n}}} & (3) \\{x_{3{nx}\; 1} = \begin{bmatrix}k_{1} \\k_{2} \\\vdots \\k_{n - 1} \\k_{n} \\b_{1} \\b_{2} \\\vdots \\b_{{2n} - 1} \\b_{2n}\end{bmatrix}} & (4) \\{d_{4{nx}\; 1} = \begin{bmatrix}\tau_{1} \\\tau_{2} \\\vdots \\\tau_{{2n} - 1} \\\tau_{2n} \\{\tau_{1} - \tau_{2}} \\{\tau_{2} - \tau_{3}} \\\vdots \\{\tau_{{2n} - 1} - \tau_{2n}} \\0\end{bmatrix}} & (5)\end{matrix}$

The matrix C consists of three sub-matrices, C₁, C₂ and C₃. C₁ can bethe main part responsible for the fitting of the spring and dashpotconstants, k and b. C₂ bounds the rate of change of the passive jointtorque and ensures smoothness in the resulting passive joint torque, andC₃ is basically a row of penalty constants, β, which penalizes largevalues of the spring and dashpot constants and thus limits themagnitudes of both. In this work, β is set to 0.1.

The origin of each virtual spring can be also added to the optimizationproblem formulation as a parameter in order to obtain a tighter passivetorque fit. Therefore, the optimization problem given by (3) can besolved iteratively for a range of values of spring origin constant, α.The solution with the least error norm can be selected as the optimalsolution.

The result of the above stated constrained optimization problem forsegment 1 can be shown in the plots in FIGS. 21A, 21B, and 21C. FIGS.21A, 21B, and 21C are the output of the decomposition for s₁ in FIG. 19showing the spring and dashpot constants and the active and passive kneetorques (Spring origin, α .is 23 degrees).

As can be seen from FIGS. 21A, 21B, and 21C, the decomposed passive partcan be very similar to the joint torque, and thus it can be stated thatthe behavior of the joint can be mainly passive. The result of thedecomposition for the segment S_(i) can be stored in R_(i) of the formgiven in Equation 6.R _(i)=[θ {dot over (θ)} τ_(pas) F _(S1) F _(S2) τ_(act)]_(2nx6)  (6)where τ_(pas)=C₁x.

The procedure presented above decomposes the joint torques into activeand passive parts. The joint torque references for the control of theprosthesis are generated by combining this active and passive torques.There are two major challenges to be solved. Firstly, the correct motionsegment must be selected. Secondly, after the motion segment is selectedat each sampling instant a new joint torque reference can be generatedusing the discrete mappings for the active and passive torque parts.

A switching system modeling approach incorporating both discrete andcontinuous states can be used for the reconstruction of the torquereference signal. The state chart shown in FIG. 22 will govern thediscrete dynamics of the controller. Since the sequence of the segmentscan be ordered (i.e., the direction of the motion for a specific gaitphase does not change), each segment can transition only to the nextone, where the transition guard function can be written as a inequalityin terms of θ and {dot over (θ)}. The transitions between segments takeno time and the dynamics of the controller are governed by the {f_(p)_(i) (θ,{dot over (θ)}); f_(a) _(i) (F_(S))} pair at each samplinginstant. The joint reference torque isτ_(ref)=τ_(a)+τ_(p) =f _(p) _(i) (θ,{dot over (θ)})+f _(a) _(i) (F_(S))  (7)

The decomposition algorithm presented above gives the result matrix, R,for each segment. The discrete data in R can be used to construct thejoint torque reference for the continuous measurements of another trialin the same gait phase. At each sampling instant of the algorithm, themeasurement vector m=[θ_(m), {dot over (θ)}_(m), F_(S1) _(—) _(m),F_(S2) _(—) _(m)]^(T) can be acquired. For the reconstruction of thepassive knee torque part, the Euclidian error norm between the [θ_(m){dot over (θ)}_(m)]^(T) and the angular position and velocities of allthe samples in that segment [θ_(i) {dot over (θ)}_(i)]^(T) can becalculated as shown in Equation 8 and stored in the vector e.e _(i)=√{square root over ((θ_(m)−θ_(i))²+({dot over (θ)}{square rootover ({dot over (θ)}_(m)−{dot over (θ)}_(i))²)}  (8)Then two elements of this vector with the least error norm are found andthe passive knee torque reference can be found as a weighted linearcombination of the passive knee torques corresponding to these points.The reconstruction of the active knee torque part is similar where only{θ,{dot over (θ)},τ_(pas)} is exchanged with {F_(S1),F_(S2),τ_(act)}.

Intent Recognition

The supervisory controller (intent recognizer) switches among differentunderlying intramodal controllers depending on the activity mode theuser imposes on the prosthesis. The intent recognizer consists of threeparts: activity mode recognizer, cadence estimator and the slopeestimator.

The activity mode recognizer detects the activity mode of the prosthesis(standing, walking, sitting, stair ascent or stair descent, etc. . . .). This can be accomplished by comparing the features which aregenerated in real time to a feature database using some machine learningand/or pattern recognition methods. The present implementation of thegait mode recognizer, which recognizes standing and walking modes, isdescribed below.

Firstly, a database which contains all the possible activity modes(standing and walking in this case) can be generated by makingexperimental trials. In the experimental trials, the user can be askedto walk or stand in different controller modes for 50 second longtrials. The socket sagittal moment above the knee joint, foot heel load,foot ball load, knee angle, knee velocity, ankle angle and anklevelocity are recorded with 1 ms sampling period. It should be noted thatother sensor signals such as accelerations and electromyographymeasurements from the residual limb can be added to the list of thesignals used for intent recognition. For example, from the recordedexperimental trials, 10000 random frames (5000 standing and 5000walking) of 100 samples length are generated for all the seven recordedsignals. The mean and the standard deviation of each frame are computed.The mean and standard deviation of signals are selected as the featuressince minimal computation can be required to obtain them. A databasecontaining 10000 samples with 14 features (mean and standard deviationof the seven signals) belonging to two classes (standing and walking)can be generated. After the database is generated, the dimension of thedatabase can be reduced from 14 to three using principal componentanalysis (PCA). Dimension reduction can be necessary because patternrecognition for high dimensional datasets can be computationallyintensive for real-time applications. After dimension reduction step,the standing and walking data can be modeled with Gaussian mixturemodels. Gaussian mixture models represent a probability distribution asa sum of several normal Gaussian distributions. The order of theGaussian mixture model for each mode can be determined according to theMinimum Description Length Criteria.

As described above, the database generation, dimension reduction and theGaussian mixture modeling are explained. For real-time decision making,overlapping frames of 100 samples can be generated at each 10 msinterval. 14 features described above are extracted from these framesand the PCA dimension reduction can be applied to these features to geta reduced three dimensional feature vector. The reduced dimensionfeatures can be fed to the Gaussian mixture models for standing andwalking and the probability of the sample vector being standing orwalking can be computed. The mode with the greater probability isselected as the instantaneous activity mode. Since one decision mightgive wrong results in some cases due to noise, disturbance, etc. . . . ,a voting scheme can be used to enhance the results. In the votingscheme, the controller activity mode is switched if and only if morethan 90 percent of the instantaneous activity mode decisions among thelast 40 decisions are a specific activity mode. Once a new activity modeis selected by the voting scheme, the underlying activity controller canbe switched to the corresponding mode.

Such an activity mode recognizer is provided by way of illustration andnot as a limitation. In the various embodiments of the invention, one ormore parts of the algorithm might be modified. For example, in someembodiments, different features such as mean, max, kurtosis, median, ARcoefficients, wavelet based features, frequency spectrum based featuresof the frame might be generated. Additionally, different dimensionreduction techniques such as linear discriminant analysis, independentcomponent analysis might be employed. Furthermore, differentclassification methods such as artificial neural networks, supportvector machines, decision trees, hidden Markov models might be used.

Cadence and Slope Estimation

Cadence estimation is accomplished by observing peak amplitudes incharacteristic signal data and then measuring the time betweensuccessive peaks. Since walking is a cyclic activity each of the sensorsignals will be periodic of cadence. The most relevant sensor signalswill contain only one characteristic amplitude peak per stride such asfoot heel load and the ball of foot load. In the real-timeimplementations, cadence estimation is accomplished by recording thefoot load after heel strike when it exceeds 400 N until the loaddecreases below 350 N. Then, the time of occurrence of the peak load inthis window is found and the previous peak time is subtracted from thenew peak time. This corresponds to stride time and can be converted tocadence (steps/min) by multiplying with 120. Once the cadence isestimated, the intent recognizer selects the corresponding middle layercontroller based on some predefined thresholds as in FIG. 23.

For example, in some embodiments, a 3D accelerometer capable ofmeasuring ±3 g accelerations is embedded into the ankle joint couplerwhere the prosthetic foot is connected. An exemplary arrangement of sucha system is shown by the schematic in FIG. 24. The accelerometermeasurements are used to estimate the ground slope. In order to estimatethe ground slope, the accelerometer data in tangential direction isused. Assuming the foot is flat on the ground, the ground slope angle,θ_(s), can be calculated as in equation (9) below.

$\begin{matrix}{\theta_{s} = {\sin^{- 1}\left( \frac{a_{t}}{g} \right)}} & (9)\end{matrix}$In Eqn. 9, g is the gravitational constant. In order to find the groundslope estimate, {circumflex over (θ)}_(s), the accelerometer data shouldbe collected while the foot is flat on the ground as determined by theheel and ball of the foot load sensors. While the foot is flat on theground, equation (1) is computed for the frame of the collected data andthe mean of this frame is outputted as the ground slope estimate,{circumflex over (θ)}_(s). Once the slope is estimated, the intentrecognizer selects the corresponding middle layer controller based onsome predefined thresholds. An exemplary state chart for such an intentrecognizer is shown in FIG. 25.

Friction and Cable Drive Based Actuation

Rather than a ballscrew and slider crank embodiment for the transmissionof torque from a motor to the ankle and/or knee units, in someembodiments of the invention, the prosthesis can incorporate a frictionand cable drive transmission embodiment. FIGS. 26A and 26B show frontand back views of an exemplary embodiment of a friction drivetransmission 2600 in accordance with an embodiment of the invention. Asshown in FIGS. 26A and 26B, the shaft 2602 of an electric motor 2604 ispreloaded against a first stage in a housing 2606, such as a largerdiameter cylinder or friction drive gear 2608, which creates sufficientfriction to transmit torque without slip. The shaft 2602 can use one ormore friction rollers 2610 to transmit the torque. The first stage ofthe friction drive can also be supplemented with a second stage. Thefriction drive gear 2608 drives a smooth pinion 2612 directly, which ispreloaded against a larger diameter cylinder or cable gear output 2614in the housing 2606, which in turn transmits torque directly to the kneeor ankle joint.

In addition to, or rather than a friction drive, the first or secondstage of the transmission can alternatively be embodied by a cable drivetransmission, in which a cable is wrapped around the circumference of alarger diameter cylinder, such as friction drive gear 2608, and alsoaround the circumference of a smaller diameter cylinder, such as pinion2612. In such embodiments, the cable is affixed to the friction drivegear 2608, and is pretensioned, using a tensioning screw 2616 or similarmeans, around both the drive gear 2608 and pinion 2612, such thatfriction between cable and pinion 2612 enables the transmission oftorque from between the pinion 2612 and drive gear 2608. In oneembodiment of a combined friction drive/cable drive transmission can beused, in which a first stage of the transmission (i.e., the frictiondrive gear 2608 connected directly to the electric motor 2604) is of thefriction drive type, while the second stage of the transmission (i.e.,the cable gear output 2614 connected directly to the knee or anklejoint) is of the cable drive type.

Applicants present certain theoretical aspects above that are believedto be accurate that appear to explain observations made regardingembodiments of the invention. However, embodiments of the invention maybe practiced without the theoretical aspects presented. Moreover, thetheoretical aspects are presented with the understanding that Applicantsdo not seek to be bound by the theory presented.

While various embodiments of the invention have been described above, itshould be understood that they have been presented by way of exampleonly, and not limitation. Numerous changes to the disclosed embodimentscan be made in accordance with the disclosure herein without departingfrom the spirit or scope of the invention. Thus, the breadth and scopeof the invention should not be limited by any of the above describedembodiments. Rather, the scope of the invention should be defined inaccordance with the following claims and their equivalents.

Although the invention has been illustrated and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art upon the reading andunderstanding of this specification and the annexed drawings. Inaddition, while a particular feature of the invention may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, to the extent that the terms “including”,“includes”, “having”, “has”, “with”, or variants thereof are used ineither the detailed description and/or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It isfurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the following claims.

What is claimed is:
 1. A method of controlling a powered joint in a legprosthesis, comprising: determining a passive joint torque componentbased on a passive function of at least one measured input, the measuredinput comprising at least one of a measured angle of a powered joint inthe leg prosthesis or a measured angular velocity of the powered joint;determining an active joint torque component based on an active functionof at least one measured external force on the leg prosthesis andindependent of at least one measured input; and computing an outputtorque for at the least one powered joint based on a superposition ofthe passive joint torque component and the active joint torquecomponent.
 2. The method of claim 1, wherein the at least one measuredinput further comprises at least one of a force or a torque exerted by auser on the leg prosthesis.
 3. The method of claim 1, wherein the atleast one measured input further comprises a measurement of an inertialmovement of the leg prosthesis.
 4. The method of claim 1, wherein thepassive function is a single-valued and odd function of at least one ofthe measured joint angle or angular velocity.
 5. The method of claim 1,wherein the passive function for the passive joint torque (τ) is givenby:τ=k(θ−θ_(e))+b{dot over (θ)} where k is a linear stiffness for thepowered joint, b is a linear damping coefficient for the powered joint,θ is the measured angle, θ_(e) is an equilibrium angle for the poweredjoint, and {dot over (θ)} is the measured angular velocity.
 6. Themethod of claim 5, wherein the linear stiffness, the linear dampingcoefficient, and the equilibrium angle are selected based on a knownbiomechanical behavior of a healthy joint.
 7. The method of claim 1,wherein the superposition comprises a difference between the passivejoint torque component and the active joint torque component.
 8. Themethod of claim 1, further comprising selecting a set of functionsapproximating the passive and active components of torque based on atype of locomotion activity.
 9. The method of claim 8, wherein each typeof locomotion activity comprises a periodic activity divided intomultiple segments, and wherein the behavior of the joint within each ofthe multiple segments can be described by a different set of functionsapproximating the passive and active components of torque.
 10. Themethod of claim 9, further comprising: switching between segments basedon a measurement associated with the at least one measured input meetinga threshold level.
 11. The method of claim 9, further comprising:switching between the different set of functions associated within asegment based on the at least one measured input meeting a thresholdlevel.
 12. The method of claim 9, wherein the functions characterizingthe passive and active portions of the joint torque are constructed viaconstrained optimization methods.
 13. The method of claim 1, wherein thepowered joint comprises at least one of a powered ankle joint or apowered knee joint.
 14. A control system for a powered joint in a legprosthesis comprising at least one of a powered ankle joint or a poweredknee joint, the system comprising: a processor; a computer readablemedium having stored thereon a plurality of instructions for causing theprocessor to perform the method comprising: determining a passive jointtorque component based on a passive function of at least one measuredinput, the measured input comprising at least one of a measured angle ofa powered joint in the leg prosthesis or a measured angular velocity ofthe powered joint; determining an active joint torque component based onan active function of at least one measured external force on the legprosthesis and independent of at least one measured input; and computingan output torque for at the least one powered joint based on asuperposition of the passive joint torque component and the active jointtorque component.
 15. The system of claim 14, wherein the at least onemeasured input further comprises at least one of a force or a torqueexerted by a user on the leg prosthesis.
 16. The system of claim 14,wherein the at least one measured input further comprises a measurementof an inertial movement of the leg prosthesis.
 17. The system of claim14, wherein the passive function is a single-valued and odd function ofat least one of the measured joint angle or angular velocity.
 18. Thesystem of claim 14, wherein the passive function for the passive jointtorque (τ) is given by:τ=k(θ−θ_(e))+b{dot over (θ)} where k is a linear stiffness for thepowered joint, b is a linear damping coefficient for the powered joint,θ is the measured angle, θ_(e) is an equilibrium angle for the poweredjoint, and {dot over (θ)} is the measured angular velocity.
 19. Thesystem of claim 14, where the superposition comprises a differencebetween the passive joint torque component and the active joint torquecomponent.
 20. The system of claim 14, further comprising selecting aset of functions approximating the passive and active components oftorque based on a type of locomotion activity.
 21. A non-transitorycomputer-readable medium having stored thereon a plurality ofinstructions for causing a computing device to perform the methodcomprising: determining a passive joint torque component based on apassive function of at least one measured input for a leg prosthesis,the measured input comprising at least one of a measured angle of apowered joint in the leg prosthesis or a measured angular velocity ofthe powered joint; determining an active joint torque component based onan active function of at least one measured external force on the legprosthesis and independent of at least one measured input; and computingan output torque for at the least one powered joint based on asuperposition of the passive joint torque component and the active jointtorque component.
 22. The computer-readable medium of claim 21, whereinthe at least one measured input further comprises at least one of aforce or a torque exerted by a user on the leg prosthesis.
 23. Thecomputer-readable medium of claim 21, wherein the at least one measuredinput further comprises a measurement of an inertial movement of the legprosthesis.
 24. The computer-readable medium of claim 21, wherein thepassive function is single-valued and odd function of at least one ofthe measured joint angle or angular velocity.
 25. The computer-readablemedium of claim 21, wherein the passive function for the passive jointtorque (τ) is given by:τ=k(θ−θ_(e))+b{dot over (θ)} where k is a linear stiffness for thepowered joint, b is a linear damping coefficient for the powered joint,θ is the measured angle, θ_(e) is an equilibrium angle for the poweredjoint, and {dot over (θ)} is the measured angular velocity.
 26. Thecomputer-readable medium of claim 21, wherein the superpositioncomprises a difference between the passive joint torque component andthe active joint torque component.
 27. The computer-readable medium ofclaim 21, wherein comprising selecting a set of functions approximatingthe passive and active components of torque based on a type oflocomotion activity.